Abstract
In this paper we present a statistical approach for localization and classification of 3-D objects in 2-D images with real heterogeneous background. Two-dimensional local feature vectors are computed directly from pixel intensities in square gray level images with the wavelet multiresolution analysis. We use three different resolution levels for the feature computation. For the first one local neighborhoods of size 8 × 8 pixels, for the second one 4 × 4 pixels, and for the third one 2 × 2 pixels are taken into account. Then we define an object area as a function of 3-D transformations and represent the feature vectors as density functions. Our localization and classification algorithm uses a combination of object models created for the three different resolutions in the training phase. Experiments made on a real data set with 42240 images show that the recognition rates are much better using the resolution combination of the wavelet transformation.
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This work was partly funded by the German Research Foundation (DFG) Graduate Research Center 3D Image Analysis and Synthesis
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References
C. Chui. An Introduction to Wavelets. Academic Press, San Diego, USA, 1992.
Ch. Gräßl, F. Deinzer, and H. Nieman. Continuous parametrization of normal distribution for improving the discrete statistical eigenspace approach for object recognition. In V. Krasnoproshin, S. Ablameyko, and J. Soldek, editors, Pattern Recognition and Information Processing 03, pages 73–77, Minsk, Belarus, Mai 2003.
R. Gross, I. Matthews, and S. Baker. Appearance-based face recognition and light-fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(4):449–465, April 2004.
M. Grzegorzek, F. Deinzer, M. Reinhold, J. Denzler, and H. Niemann. How fusion of multiple views can improve object recognition in real-world environments. In T. Ertl, B. Girod, G. Greiner, H. Niemann, H.-P. Seidel, E. Steinbach, and R. Westermann, editors, Vision, Modeling, and Visualization 2003, pages 553–560, Munich, Germany, November 2003. Aka/IOS Press, Berlin, Amsterdam.
J. Kerr and P. Compton. Toward generic model-based object recognition by knowledge acquisition and machine learning. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, pages 9–15, Acapulco, Mexico, August 2003.
S. Mallat. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7):674–693, July 1989.
S. Park, J. Lee, and S. Kim. Content-based image classification using a neural network. Pattern Recognition Letters, 25(3):287–300, February 2004.
M. Reinhold. Robust Probabilistic Appearance-Based Object Recognition. Logos Verlag, Berlin, Germany, 2004.
A. R. Webb. Statistical Pattern Recognition. John Wiley & Sons Ltd, Chichester, England, 2002.
M. Zobel, J. Denzler, B. Heigl, E. Nöth, D. Paulus, J. Schmidt, and G. Stemmer. Mobsy: Integration of vision and dialogue in service robots. Machine Vision and Applications, 14(1):26–34, 2003.
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© 2005 Springer-Verlag Berlin Heidelberg
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Grzegorzek, M., Reinhold, M., Niemann, H. (2005). Feature Extraction with Wavelet Transformation for Statistical Object Recognition. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_17
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DOI: https://doi.org/10.1007/3-540-32390-2_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25054-8
Online ISBN: 978-3-540-32390-7
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